This thesis describes DesignTag, a system which can be used to identify electronic design intellectual property (IP) within operating integrated circuits (ICs). The ability to accurately identify IP within ICs has several important applications, including IP theft detection, identifying unmarked ICs and detecting counterfeit devices. Current and proposed methods for identifying IP within ICs are either inadequate for many applications, or are expensive, time consuming and difficult to use. In contrast, the DesignTag system is useful for a wide range of applications, is easy to use, and allows inexpensive and fast detection of IP. The DesignTag system consists of a small “tag” circuit which is added to a piece of IP and a tag detection sub-system which is used to detect signals transmitted by on-chip tags. These signals are transmitted using a thermal side channel, a novel approach in which information is communicated by varying the temperature of an IC package. In addition to discussing the DesignTag system as a whole, this thesis focuses on the development of the tag detection sub-system, adapting concepts from CDMA wireless communications to enable the detection of thermally transmitted tag signals.

As CMOS scales down to sub-50 nm, it faces critical dimensions of charge and matter granularities, leading to the drastic increase of device parameter dispersion, named statistical variability, which is one of the main contemporary challenges for further downscaling and makes each device atomistically different leading to broad dispersion of their electrical characteristics. In addition, device reliability concerns gain inertia; among them Bias Temperature Instability (BTI) shortens device lifetime by trapping charges in defect states of the insulator or at the interface. The interplay between statistical variability and BTI results in more variations on device performance and thus greatly affect circuit performance. In turn design methodologies must evolve towards variability and reliability aware design. To do so statistical compact models including both the effects of statistical variability and BTI-induced ageing are required for the large-scale statistical circuit simulation of variability and reliability. In this study, the application of accurate compact models, that describe performance variation in the presence of both statistical variability and reliability at arbitrary BTI-induced ageing levels, to SRAM circuit simulation is described. Both SRAM cell stability and write performance are evaluated and it is seen that, due to the accurate description of device performance distributions provided by the compact models and the sensitivity of these SRAM performance metrics on device performance, the approach presented here is better suited to high-sigma statistical circuit analysis than conventional approaches based upon assumed Gaussian distributions. The approach is demonstrated using a 25 nm gate length bulk MOSFET whose performance variation is obtained from statistical TCAD simulation using the GSS simulator GARAND. The simulated performance data is then used directly as the target for BSIM4 compact model extraction that ensures device figures of merit are well resolved for each device in a statistical ensemble. The distribution of compact model parameters is then generalised into an algebraic form using Generalized Lambda Distribution (GLD) methods, so that a sufficiently large number of compact models can later be generated and interpolated at arbitrary ageing levels. Finally compact models generated in this way are used to evaluate SRAM write performance and stability under the influence of statistical variability and BTI-induced ageing.

The large hadron collider (LHC) uses the most energetic and highest luminosity man made proton beams on Earth. The high luminosity (HL-LHC) [1] upgrade aims to increase the levelled luminosity of the LHC by a factor of five, to 5 · 1034cm−2 s −1 , by increasing the bunch population from 1 to 2.22·1011 protons, and decreasing emittance, and β ∗ . Thus the stored beam energy increases from ≈ 362 MJ to ≈ 675 MJ per beam. All synchrotrons encounter unavoidable proton losses. Protons that populate the beam halo pose a threat to the performance and lifetime of certain hardware, such as superconducting magnets, which in the LHC may be quenched by an impact of ≈ 1 · 106 protons [2]. A multi stage collimation system must operate at an efficiency such that no more than 2 · 10−5% of protons incident on collimators may escape and impact upon these magnets [3]. To predict and protect against proton losses in the HL-LHC, collimation simulations must be performed. MERLIN, a C++ accelerator physics library, has been updated to carry out such simulations for the HL-LHC. Novel materials such as molybdenum graphite have been investigated as collimator materials, and a novel technique - collimation enhancement via a hollow electron lens (HEL) - has been studied. Using detailed simulations the performance and operation of possible collimation upgrades are explored.

This research focuses on classification of categorical events using advanced statistical models. Primarily utilised to detect and identify individual component faults and deviations from normal healthy operation of reciprocating compressors. Effective monitoring of condition ensuring optimal efficiency and reliability whilst maintaining the highest possible safety standards and reducing costs and inconvenience due to impaired performance. Variability of operating conditions being revealed through examination of vibration signals recorded at strategic points of the process. Analysis of these signals informing expectations with respect to tolerable degrees of imperfection in specific components. Isolating inherent process variability from extraneous variability affords reliable means of ascertaining system health and functionality. Vibration envelope spectra offering highly responsive model parameters for diagnostic purposes. This thesis examines novel approaches to alleviating the computational burdens of large data analysis through investigation of the potential input variables. Three methods are investigated as follows: Method one employs multivariate variable clustering to ascertain homogeneity amongst input variables. A series of heterogeneous groups being formed from each of which explanatory input variables are selected. Data reduction techniques, method two, offer an alternative means of constructing predictive classifiers. A reduced number of reconstructed explanatory variables provide enhanced modelling capabilities ensuring algorithmic convergence. The final novel approach proposed combines both these methods alongside wavelet data compression techniques. Simplifying number of input parameters and individual signal volume whilst retaining crucial information for deterministic supremacy.

Ion irradiation modification of silicon nanowires has been explored in-situ using the Microscope and Ion Accelerator for Materials Investigations (MIAMI) facility at the University of Huddersfield. Ion irradiation experiments were designed using the Stopping and Range of Ions in Matter (SRIM) Monte Carlo computer code. A multislice SRIM method was developed to estimate the damage and ion implantation in a nanowires geometry and a code was developed to incorporate SRIM into MatLab. In-situ Transmission Electron Microscopy (TEM) has been used to explore the ion-beam-induced bending of silicon nanowires under different irradiation conditions and the underlying mechanisms which drive nanowire bending have been identified. Furthermore, a tipping point for direction reversal of bending under different irradiation conditions has been identified. Recrystallisation of silicon nanowires was also investigated using thermal annealing. The effects of 7 keV Xe+ ions on silicon nanowires have been investigated under industrial processing conditions. Structural and morphological changes of silicon nanowires have been observed. These include nanowire bending, amorphisation, bubble formation and sputtering. The depth of damage has also been measured experimentally and was compared with the predictive damage using SRIM. In order to calculate the temperature along a nanowire during an in-situ TEM heating experiment, the relevant parameters have been found from the literature which will be used to set up a finite element model. Atomic Force Microscopy (AFM) was used to measure the surface roughness of silicon nanowires and molybdenum grids. Other parameters of interest include the Hamaker’s constant for the Van der Waals forces as well as the Young’s modulus, thermal conductivity and specific heat capacity of silicon nanowires and the ultra-thin oxide layer on their surface.

This thesis forms a report of the progress made by the author in developing a rotating cylinder viscometer for measurement at elevated temperature and pressure. Initially, some proposals of Whitelaw (5) were tested in a preliminary design of viscometer. Experience gained with this instrument made possible the construction of a viscometer from which the viscosity of liquid water has been obtained in the range 12° to 116°C and at pressures up to 230 Kgf/cm2. Important features of this design, which have been successfully applied, include a null technique for measuring the angular deflection of the stationary cylinder and the use of a high pressure mechanical face seal for the shaft driving the rotating cylinder. A vacuum rig was constructed for measuring the torsional properties of suspension wires used in the viscometer. The temperature of this rig could be varied which allowed the torsional stiffness of tungsten wire to be determined experimentally up to 150°C. Viscosity measurements at 1 atm. pressure were fitted to a modified Arrhenius equation of the form η = AeB/T-T0 The standard deviation of experimental values from this equqtion was 7 x 10-3 cP which, at 20°C, amounts to approximately 0.7% of the measured viscosity. The absolute value of viscosity given by this equation was approximately higher than the presently accepted standard value at this temperature. At temperatures above 50°C the results obtained appear to be slightly low, the possible reasons for which have been explained. Results at elevated pressure were made over a similar temperature range although the majority of determinations were made at temperatures below 20°C. These low temperature measurements lend support to the findings of Bett and Cappi (48) rather than those of Home and Johnson (52) who have made the most recent experimental investigations in this field. The results obtained are regarded as preliminary in so far as a systematic coverage of the whole range has not yet been made. A second, partially completed, instrument is described which will extend the rsinge of measurement to 400°C and 1000 Kgf/cm2. A system of radial diffraction gratings producing moire fringes has been incorporated which, it is expected, will improve the precision with which the deflection of the inner cylinder may be obtained. Since the dynamic method of measuring the torsional stiffness of the wire has been used a number of tests were performed to investigate the effect of varying the mass, inertia and amplitude of a torsional pendulum. The results of these tests are described in a number of Appendices, the main conclusion being that the amplitude effect is significant and a correction factor may be derived which can be applied to the angular deflection of the wire when it is used statically.

The age of big digital data is emerged and the size of generating data is rapidly increasing in a millisecond through the Internet of Things (IoT) and Internet of Everything (IoE) objects. Specifically, most of today’s available data are generated in a form of streams through different applications including sensor networks, bioinformatics, smart airport, smart highway traffic, smart home applications, e-commerce online shopping, and social media streams. In this context, processing and mining such high volume of data stream becomes one of the research priority concern and challenging tasks. On the one hand, processing high volumes of streaming data with low-latency response is a critical concern in most of the real-time application before the important information can be missed or disregarded. On the other hand, detecting events from data stream is becoming a new research challenging task since the existing traditional anomaly detection method is mainly focusing on; a) limited size of data, b) centralised detection with limited computing resource, and c) specific anomaly detection types of either point or collective rather than the Contextual behaviour of the data. Thus, detecting Contextual events from high sequence volume of data stream is one of the research concerns to be addressed in this thesis. As the size of IoT data stream is scaled up to a high volume, it is impractical to propose existing processing data structure and anomaly detection method. This is due to the space, time and the complexity of the existing data processing model and learning algorithms. In this thesis, a novel distributed anomaly detection method and algorithm is proposed to detect Contextual behaviours from the sequence of bounded streams. Capturing event streams and partitioning them over several windows to control the high rate of event streams mainly base on, the proposed solution firstly. Secondly, by proposing a parallel and distributed algorithm to detect Contextual anomalous event. The experimental results are evaluated based on the algorithm’s performances, processing low-latency response, and detecting Contextual anomalous behaviour accuracy rate from the event streams. Finally, to address scalability concerned of the Contextual events, appropriate computational metrics are proposed to measure and evaluate the processing latency of distributed method. The achieved result is evidenced distributed detection is effective in terms of learning from high volumes of streams in real-time.

The topic of this study is self-tracking. To “self-track” is to quantify steps, calories, heart rate, social interactions, geo-location, posture, and so on. The few monographs, academic papers, and conferences, concerned with this topic often deliver similar philosophical representations of self-tracking based on shared material and socio-cultural assumptions. Generally, these assumptions are wedded to a series of essentialist, absolutist, and deterministic tropes, as well as to neo-humanist and more traditionally humanist forms of discourse. The present study argues that these established discourses play against the predominant tendencies of much contemporary philosophical thought. Contemporary thought is more post humanist than humanist, draws attention to mediation more than media, qualities of indeterminacy more than determinacy, and dynamism more than stasis. The lack of imagination in current discourses of self-tracking, as well as their disengagement with contemporary thought, has resulted in the most pertinent philosophical insight of the phenomena of self-tracking being overlooked: namely the permanence of change. Unique to discourses of self-tracking, this study foregrounds the permanence of change as an already kinetic and transformative aspect of the natural world. As a result of this unique contribution, this study backgrounds the monotony of substance-metaphysical thought as a resistance to the established trends of self-tracking discourses. There are four parts to this process-oriented world view that make up the main features of this research. Firstly, the missing dynamism of self-tracking is rendered actual by its reframing in an event-based ontology, therefore emphasising the emergence, mutation, and inevitable excesses of self-tracking phenomena. Secondly, self-tracking is critically situated in anti-representational terms by focusing on the recent processual-turn in new media theory. Thirdly, via a genealogical method, self-tracking is rearticulated as something dynamic, with a long and plural history of transformative relations. Finally, by emphasising the political, relational, and fictional, a re-imagined speculative alternative to self-tracking is brought to focus through the optic of contemporary design theory.

Medical education and practical training in surgery is changing, by shifting from an on the job learning paradigm, which possesses problems such as that it is unpredictable, dependent on clinical needs and that patient safety may be jeopardised, to an evidence-based surgical skills training driven by curricular needs, and acquiring basic surgical skills prior to assisting in the operating theatre and thus reducing operation duration. Towards achieving this goal, virtual reality (VR) simulators are used in minimally invasive surgery for technical skills training at the beginning of the learning curve, but have not yet been adapted for open surgery due to its complexity for simulation. This thesis investigated the potential of using a VR simulator for training in orthopaedic hand surgery, with an emphasis on providing a meaningful, effective and motivating addition to current training methods for surgical procedures. A review of literature, preliminary research projects and currently available surgical systems revealed limited results on whether a VR simulation of orthopaedic hand surgery could be created, fulfilling the needs of medical experts. Therefore, a study investigating the current state of medical education and to understand the expectations on such a simulator was carried out, which resulted in the identification of promising medical scenarios for simulation (such as carpal tunnel release, distal radius fracture treatment or surgical incision) and in requirements for its development. Different software frameworks have been evaluated for their ability for use by analysing five developed demonstrators, with the result that a custom implementation of a six-degrees-of-freedom haptic algorithm was required. By following a human-centred design approach, a VR surgical simulator with inbuilt objective measures of assessment has been developed, allowing applying a plate, drilling holes, measuring their lengths, inserting screws and taking virtual X-rays, supported by haptic feedback for increased realism and teaching aspects not possible by common computer-based simulators, such as feeling the resistance when drilling through the cortical bone. By close collaboration with medical experts and following user interface design principles, a carried out medical evaluation of the simulator showed that the simulator was well-received by the targeted young doctors and medical students, that relevant aspects of the implemented medical scenario are taught and that the users’ performance can be assessed. The findings of this work showed that it is possible to create an interactive VR simulator aimed at early stages to learn basic orthopaedic principles of open surgery using the example of the treatment of distal radius fractures in a meaningful manner. It addresses issues in the current medical education and enables learning educational objectives repeatedly in reusable medical scenarios and in a safe and controlled environment, without the risk of harming patients, and thus contributing to improved quality and patient safety when proceeding to the operating theatre.

The value of Remote Condition Monitoring for the real-time evaluation of the structural integrity of critical components is undeniable. Fibre-reinforced polymer composites are a class of materials which offer significant advantages over conventional metal alloys used for manufacturing load bearing structures in cases where weight and/or energy consumption need to be kept to a minimum, for example automotive and aerospace applications. This is due to the excellent strength to weight ratio that FRPCs exhibit. However, their strongly anisotropic microstructure of poses significant challenges for Non-Destructive Evaluation of the actual structural health of components made from such materials. Acoustic Emission is a passive condition monitoring technique based on the detection of elastic stress waves emitted when damage evolves in a structure. Conventional piezoelectric AE sensors need to be surface-mounted as their embedding in FRPCs is impractical. Fibre Optic Acoustic Emission Sensors (FOAES) offer a distinct advantage since they are light weight, have small size and can be effectively embedded in composite laminates. Moreover, they can be multiplexed with the entire structure being monitored more effectively. This study has focused in the evaluation of the manufacturing process and characterisation of FOAES. Comparison of their performance with conventional commercial sensors was carried out.